Large-Scale Time Series Analytics

Novel Approaches for Generation and Prediction
  • Martin Hahmann
  • Claudio HartmannEmail author
  • Lars Kegel
  • Wolfgang Lehner


More and more data is gathered every day and time series are a major part of it. Due to the usefulness of this type of data, it is analyzed in many application domains. While there already exists a broad variety of methods for this task, there is still a lack of approaches that address new requirements brought up by large-scale time series data like cross-domain usage or compensation of missing data. In this paper, we address these issues, by presenting novel approaches for generating and forecasting large-scale time series data.


Data analytics Time series generation Time series forecasting Big Data 



This work was funded by the German Federal Ministry of Education and Research within the project Competence Center for Scalable Data Services and Solutions Phase 1—ScaDS Dresden/Leipzig (BMBF 01IS14014A).


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Copyright information

© Gesellschaft für Informatik e.V. and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Database Systems GroupTechnische Universität DresdenDresdenGermany

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